In 2023, the youngest technology professionals in the age range of 18-25 were the most receptive to new artifical intelligence (AI) tools, with a weekly adoption rate of about ** percent. The adoption rate goes down as the age of the IT professionals increases.

The number of AI tools users in the 'AI Tool Users' segment of the artificial intelligence market worldwide was modeled to stand at ************** in 2024. Following a continuous upward trend, the number of AI tools users has risen by ************** since 2020. Between 2024 and 2031, the number of AI tools users will rise by **************, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on Artificial Intelligence.

AI tools continue expanding across daily life and business. From personalized learning to automated workflows, they fuel efficiency and reshape how people work. In healthcare, AI assists doctors in diagnostics, and in banking, it automates routine transactions and customer support. Readers can dive deeper into how AI is moving from...

Cloud based services are reported to be the most popular generative artificial intelligence (AI) tool currently in use, with ** percent of those surveyed worldwide reporting that they use it. Far behind are local or offline solutions with a share of ** percent.

As of September 2024, around ** percent of adult artificial intelligence (AI) tool users have used ChatGPT, making it the most popular AI-powered tool in the country. Google Gemini and Meta AI were cited by ** and ** percent of respondents each, while Snapchat My AI was mentioned by around ** percent of respondents. Around ** percent of the interviewees stated they had used artificial intelligence (AI) tools that year.

Technology professionals in Asia had the highest weekly adoption of artificial intelligence (AI) tools with a rate of **** percent in 2023. In contrast, the United States had the lowest adoption rate of AI tools with only about ** percent.

In 2023, AI tools were used daily by IT professionals across various fields. In that year, over ** percent of machine learning engineers globally reported using these tools every day, while data scientists followed closely, with around ** percent stating daily usage. Back-end developers and full-stack developers reported slightly lower usage, with **** percent and **** percent respectively stating that they use AI tools daily.

The data set records the perceptions of Bangladeshi university students on the influence that AI tools, especially ChatGPT, have on their academic practices, learning experiences, and problem-solving abilities. The varying role of AI in education, which covers common usage statistics, what AI does to our creative abilities, its impact on our learning, and whether it could invade our privacy. This dataset reveals perspective on how AI tools are changing education in the country and offering valuable information for researchers, educators, policymakers, to understand trends, challenges, and opportunities in the adoption of AI in the academic contex.

Methodology Data Collection Method: Online survey using google from Participants: A total of 3,512 students from various Bangladeshi universities participated. Survey Questions:The survey included questions on demographic information, frequency of AI tool usage, perceived benefits, concerns regarding privacy, and impacts on creativity and learning.

Sampling Technique: Random sampling of university students Data Collection Period: June 2024 to December 2024

Privacy Compliance This dataset has been anonymized to remove any personally identifiable information (PII). It adheres to relevant privacy regulations to ensure the confidentiality of participants.

For further inquiries, please contact: Name: Md Jhirul Islam, Daffodil International University Email: jhirul15-4063@diu.edu.bd Phone: 01316317573

This dataset is a synthetically generated simulation of 500 high school students from around the world and their usage of AI tools in 2025.

Features included:

• Demographics: Country, grade
• Tool Usage: ChatGPT, Google Gemini, Grammarly, Quillbot, Notion AI, etc.
• Usage Metrics:
  • Frequency of usage (Daily / Weekly / Monthly / Never)
  • Primary purpose (Homework, Notes, Coding Help, Summarization, Writing)
  • Perceived usefulness (scale 1–5)
• Open-ended AI Usage: Field where students mention other tools they use

Countries Represented:

USA, UK, India, Canada, Australia, Germany, Brazil, South Korea, Nigeria, Japan

Note:

This dataset is synthetic and generated using probabilistic logic and patterns — no personal or survey data was collected.

Use Cases:

• Visualization & EDA
• Market insights for edtech/AI products
• NLP tasks with open-ended responses
• Classification: Predicting AI usage patterns

As of April 2024, search engines powered by artificial intelligence (AI) results were the preferred search tools among the majority of teens in the United States, with 20 percent of them using them every day. Around 27 percent of U.S. teens reported using chatbots for online search daily, while around 20 percent of survey respondents stated that they use AI tools for image generation each day. Overall, seven percent reported using image generators multiple times in a week, while five percent used video generators as often.

A. SUMMARY This dataset contains a preliminary inventory of artificial intelligence (AI) systems declared by departments within the City and County of San Francisco (CCSF), as part of compliance with Chapter 22J of the Administrative Code. Chapter 22J requires departments and vendors to answer 22 standardized questions about AI technologies that are in use—excluding those used solely for internal administration or cybersecurity purposes. This is an initial release and may not yet reflect a complete list. A comprehensive, citywide inventory will be published by January 2026. For more information, see the full ordinance: Chapter 22J – Artificial Intelligence Tools B. HOW THE DATASET IS CREATED Each City department is required to annually submit an AI inventory as part of their compliance with Chapter 22J. Departments complete a standardized intake form that captures key details about each AI system in use or under consideration. The submitted inventories are reviewed and consolidated by the Department of Technology C. UPDATE PROCESS The full dataset of AI technologies and uses will be published by Jan 2026 and updated every two years D. HOW TO USE THIS DATASET Each row represents an individual AI technology reported by a City department, along with details about its use. The dataset includes 22 columns corresponding to the required questions outlined in Chapter 22J

Artificial intelligence (AI) is a technology that enables products to be combined with new features and create innovative customer experiences . A lot of businesses have embraced various AI tools to offer customer care interactions. Research gaps arise from an unclear picture of how customers' experience with online shopping will be affected by the experience and usage of AI tools. This study aims to predict satisfied online shoppers based on their usage experience with AI tools, by leveraging data mining methods and machine learning techniques. Data was collected from India, China, and Canada in 2021 and 2022 by distributing online survey to online shoppers with exposure to AI tools. Five machine learning algorithms; decision tree, random forest, naïve bayes, gradient boosted tree and multilayer perceptron neural network techniques were applied and compared to predict satisfied shoppers using. Overall, all the models showed a prediction accuracy of more than 86.5% f-score value and random forest outperformed with 91.5% f-score value. The findings demonstrated that the online retail business can identify satisfied customers with 91.5% accuracy using machine learning. Business can derive such data-driven actionable knowledge from integrating machine learning into their operations, resulting in a more satisfied customer base and a more efficient and competitive business model.

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Artificial Intelligence (AI) Market In Education Sector Size 2025-2029

The artificial intelligence (ai) market in education sector size is forecast to increase by USD 4.03 billion at a CAGR of 59.2% between 2024 and 2029.

The Artificial Intelligence (AI) market in the education sector is experiencing significant growth due to the increasing demand for personalized learning experiences. Schools and universities are increasingly adopting AI technologies to create customized learning paths for students, enabling them to progress at their own pace and receive targeted instruction. Furthermore, the integration of AI-powered chatbots in educational institutions is streamlining administrative tasks, providing instant support to students, and enhancing overall campus engagement. However, the high cost associated with implementing AI solutions remains a significant challenge for many educational institutions, particularly those with limited budgets. Despite this hurdle, the long-term benefits of AI in education, such as improved student outcomes, increased operational efficiency, and enhanced learning experiences, make it a worthwhile investment for forward-thinking educational institutions. Companies seeking to capitalize on this market opportunity should focus on developing cost-effective AI solutions that cater to the unique needs of educational institutions while delivering measurable results. By addressing the cost challenge and providing tangible value, these companies can help educational institutions navigate the complex landscape of AI adoption and unlock the full potential of this transformative technology in education.

What will be the Size of the Artificial Intelligence (AI) Market In Education Sector during the forecast period?

Request Free SampleArtificial Intelligence (AI) is revolutionizing the education sector by enhancing teaching experiences and delivering personalized learning. AI technologies, including deep learning and machine learning, power adaptive learning platforms and intelligent tutoring systems. These systems create learner models to provide personalized recommendations and instructional activities based on individual students' needs. AI is transforming traditional educational models, enabling intelligent systems to handle administrative tasks and data analysis. The integration of AI in education is leading to the development of intelligent training software for skilled professionals. Furthermore, AI is improving knowledge delivery through data-driven insights and enhancing the learning experience with interactive and engaging pedagogical models. AI technologies are also being used to analyze training formats and optimize domain models for more effective instruction. Overall, AI is streamlining administrative tasks and providing personalized learning experiences for students and professionals alike.

How is this Artificial Intelligence (AI) In Education Sector Industry segmented?

The artificial intelligence (ai) in education sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHigher educationK-12Learning MethodLearner modelPedagogical modelDomain modelComponentSolutionsServicesApplicationLearning platform and virtual facilitatorsIntelligent tutoring system (ITS)Smart contentFraud and risk managementOthersTechnologyMachine LearningNatural Language ProcessingComputer VisionSpeech RecognitionGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalySpainUKAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilMiddle East and AfricaUAE

By End-user Insights

The higher education segment is estimated to witness significant growth during the forecast period.The global education sector is witnessing significant advancements with the integration of Artificial Intelligence (AI). AI technologies, including Machine Learning (ML), are revolutionizing various aspects of education, from K-12 schools to higher education and corporate training. Intelligent Tutoring Systems and Adaptive Learning Platforms are increasingly popular, offering Individualized Instruction and Personalized Learning Experiences based on each student's Learning Pathways and Skills Gap. AI-enabled solutions are enhancing Student Engagement by providing Interactive Learning Tools and Real-time communication, while AI platforms and startups are developing Smart Content and Tailored Content for Remote Learning environments. AI is also transforming administrative tasks, such as Assessment processes and Data Management, by providing Personalized Recommendations and Automated Grading. Universities and educational institutions are leveraging AI for Pedagogical model development and Virtual Classrooms, offering Educational Experiences and Virtual support. AI is also being used f

In a 2025 survey, around ** percent of respondants claimed to use AI tools intentionally on a daily basis either for personal use, work or study purposes. Similarly, ** percent reported to never use AI tools

AI Detection Tool Market Outlook

The global AI Detection Tool market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 7.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 19.1% during the forecast period. The rapid advancement in artificial intelligence technologies and the increasing need for robust AI detection tools to mitigate risks such as data breaches and algorithmic bias are key factors driving this growth.

One of the primary growth factors for the AI Detection Tool market is the increasing prevalence of AI applications across various sectors such as finance, healthcare, and media. As AI systems become more integrated into critical decision-making processes, the need for tools that can detect and audit AI algorithms for fairness, accuracy, and transparency becomes paramount. Additionally, regulatory bodies worldwide are beginning to enforce stringent guidelines that mandate the use of AI detection tools to ensure compliance with ethical standards and data protection laws.

Another significant growth driver is the rising awareness about data security and privacy concerns. With the increasing volume of data being processed by AI systems, the potential for misuse and breaches has escalated. AI detection tools play a crucial role in identifying and mitigating these risks, thereby protecting sensitive information. This growing focus on data security is expected to propel the demand for AI detection solutions across various industries, further contributing to market growth.

Technological advancements in AI and machine learning are also contributing to the expansion of the AI Detection Tool market. Innovations in these fields are leading to the development of more sophisticated and efficient detection tools that can better analyze complex data sets and identify anomalies. The continuous improvement in AI detection capabilities is likely to attract more enterprises to adopt these tools, thus driving market growth.

From a regional perspective, North America is anticipated to hold the largest market share due to the high adoption rate of AI technologies and the presence of major AI solution providers. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by the rapid digital transformation in emerging economies such as China and India. The increasing investment in AI research and development in these countries is also contributing to the regional market growth.

Component Analysis

The AI Detection Tool market by component can be segmented into software, hardware, and services. The software segment is expected to dominate the market due to the increasing demand for advanced AI detection algorithms and platforms that can be integrated into existing systems. Software solutions offer flexibility and scalability, making them a preferred choice for enterprises looking to enhance their AI detection capabilities.

In the context of data security, a Data Classification Tool becomes an essential asset for organizations aiming to manage and protect their data effectively. As AI detection tools are employed to safeguard sensitive information, data classification tools help in categorizing data based on its sensitivity and importance. This categorization enables organizations to apply appropriate security measures and comply with data protection regulations. By integrating data classification tools with AI detection systems, enterprises can enhance their data governance strategies, ensuring that sensitive data is adequately protected against unauthorized access and breaches. This synergy not only strengthens data security frameworks but also supports compliance with evolving regulatory landscapes, making data classification tools a vital component in the broader AI detection ecosystem.

Hardware components, on the other hand, are crucial for the effective deployment of AI detection tools. These include specialized processors and sensors that enable real-time data analysis and anomaly detection. While the hardware segment may not be as large as the software segment, it is still expected to witness significant growth due to the ongoing advancements in AI-specific hardware technologies.

Services form an integral part of the AI Detection Tool market, encompassing consulting, integration, and support services. As organizations increasingly adopt AI detection tools, th

IntroductionProviding one-on-one support to large cohorts is challenging, yet emerging AI technologies show promise in bridging the gap between the support students want and what educators can provide. They offer students a way to engage with their course material in a way that feels fluent and instinctive. Whilst educators may have views on the appropriates for AI, the tools themselves, as well as the novel ways in which they can be used, are continually changing.MethodsThe aim of this study was to probe students' familiarity with AI tools, their views on its current uses, their understanding of universities' AI policies, and finally their impressions of its importance, both to their degree and their future careers. We surveyed 453 psychology and sport science students across two institutions in the UK, predominantly those in the first and second year of undergraduate study, and conducted a series of five focus groups to explore the emerging themes of the survey in more detail.ResultsOur results showed a wide range of responses in terms of students' familiarity with the tools and what they believe AI tools could and should not be used for. Most students emphasized the importance of understanding how AI tools function and their potential applications in both their academic studies and future careers. The results indicated a strong desire among students to learn more about AI technologies. Furthermore, there was a significant interest in receiving dedicated support for integrating these tools into their coursework, driven by the belief that such skills will be sought after by future employers. However, most students were not familiar with their university's published AI policies.DiscussionThis research on pedagogical methods supports a broader long-term ambition to better understand and improve our teaching, learning, and student engagement through the adoption of AI and the effective use of technology and suggests a need for a more comprehensive approach to communicating these important guidelines on an on-going basis, especially as the tools and guidelines evolve.

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

According to Cognitive Market Research, the global Ai Training Data market size is USD 1865.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.50% from 2023 to 2030.

The demand for Ai Training Data is rising due to the rising demand for labelled data and diversification of AI applications.
Demand for Image/Video remains higher in the Ai Training Data market.
The Healthcare category held the highest Ai Training Data market revenue share in 2023.
North American Ai Training Data will continue to lead, whereas the Asia-Pacific Ai Training Data market will experience the most substantial growth until 2030.

Market Dynamics of AI Training Data Market

Key Drivers of AI Training Data Market

Rising Demand for Industry-Specific Datasets to Provide Viable Market Output

A key driver in the AI Training Data market is the escalating demand for industry-specific datasets. As businesses across sectors increasingly adopt AI applications, the need for highly specialized and domain-specific training data becomes critical. Industries such as healthcare, finance, and automotive require datasets that reflect the nuances and complexities unique to their domains. This demand fuels the growth of providers offering curated datasets tailored to specific industries, ensuring that AI models are trained with relevant and representative data, leading to enhanced performance and accuracy in diverse applications.

In July 2021, Amazon and Hugging Face, a provider of open-source natural language processing (NLP) technologies, have collaborated. The objective of this partnership was to accelerate the deployment of sophisticated NLP capabilities while making it easier for businesses to use cutting-edge machine-learning models. Following this partnership, Hugging Face will suggest Amazon Web Services as a cloud service provider for its clients.

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Advancements in Data Labelling Technologies to Propel Market Growth

The continuous advancements in data labelling technologies serve as another significant driver for the AI Training Data market. Efficient and accurate labelling is essential for training robust AI models. Innovations in automated and semi-automated labelling tools, leveraging techniques like computer vision and natural language processing, streamline the data annotation process. These technologies not only improve the speed and scalability of dataset preparation but also contribute to the overall quality and consistency of labelled data. The adoption of advanced labelling solutions addresses industry challenges related to data annotation, driving the market forward amidst the increasing demand for high-quality training data.

In June 2021, Scale AI and MIT Media Lab, a Massachusetts Institute of Technology research centre, began working together. To help doctors treat patients more effectively, this cooperation attempted to utilize ML in healthcare.

www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/

Restraint Factors Of AI Training Data Market

Data Privacy and Security Concerns to Restrict Market Growth

A significant restraint in the AI Training Data market is the growing concern over data privacy and security. As the demand for diverse and expansive datasets rises, so does the need for sensitive information. However, the collection and utilization of personal or proprietary data raise ethical and privacy issues. Companies and data providers face challenges in ensuring compliance with regulations and safeguarding against unauthorized access or misuse of sensitive information. Addressing these concerns becomes imperative to gain user trust and navigate the evolving landscape of data protection laws, which, in turn, poses a restraint on the smooth progression of the AI Training Data market.

How did COVID–19 impact the Ai Training Data market?

The COVID-19 pandemic has had a multifaceted impact on the AI Training Data market. While the demand for AI solutions has accelerated across industries, the availability and collection of training data faced challenges. The pandemic disrupted traditional data collection methods, leading to a slowdown in the generation of labeled datasets due to restrictions on physical operations. Simultaneously, the surge in remote work and the increased reliance on AI-driven technologies for various applications fueled the need for diverse and relevant training data. This duali...

The most popular task that people used artificial intelligence (AI) tools for worldwide in 2023 was to learn new things, with about ** percent of respondents reporting that they use it regularly. A further ** percent reported that they used AI tools for learning new things from time to time.